lime-cakes / funie-gan Goto Github PK

TensorFlow and PyTorch implementations of the paper Fast Underwater Image Enhancement for Improved Visual Perception (RA-L 2020) and other GAN-based models.

• Training pipelines for FUnIE-GAN and UGAN (original repo) on TensorFlow (Keras) and PyTorch
• Modules for image quality analysis based on UIQM, SSIM, and PSNR (see Evaluation)

Enhanced underwater imagery	Improved detection and pose estimation

• Provides competitive performance for underwater image enhancement
• Offers real-time inference on single-board computers
  • 48+ FPS on Jetson AGX Xavier, 25+ FPS on Jetson TX2
  • 148+ FPS on Nvidia GTX 1080
• Suitable for underwater robotic deployments for enhanced vision

• Paper: https://ieeexplore.ieee.org/document/9001231
• Preprint: https://arxiv.org/pdf/1903.09766.pdf
• Datasets: http://irvlab.cs.umn.edu/resources/euvp-dataset
• Bibliography entry for citation:

   @article{islam2019fast,
       title={Fast Underwater Image Enhancement for Improved Visual Perception},
       author={Islam, Md Jahidul and Xia, Youya and Sattar, Junaed},
       journal={IEEE Robotics and Automation Letters (RA-L)},
       volume={5},
       number={2},
       pages={3227--3234},
       year={2020},
       publisher={IEEE}
   }
  

• Sea-Thru (project page)
• Haze-line-aware Color Restoration (code)
• Local Color Mapping Combined with Color Transfer (code)
• Real-time Model-based Image Color Correction for Underwater Robots (code)
• All-In-One Underwater Image Enhancement using Domain-Adversarial Learning (code)
• Difference Backtracking Deblurring Method for Underwater Images
• Guided Trigonometric Bilateral Filter and Fast Automatic Color correction
• Red-channel Underwater Image Restoration (code)

• Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions
• Human-Visual-System-Inspired Underwater Image Quality Measures
• An Underwater Image Enhancement Benchmark Dataset and Beyond
• An Experimental-based Review of Image Enhancement and Restoration Methods (code)
• Diving Deeper into Underwater Image Enhancement: A Survey
• A Revised Underwater Image Formation Model